The denied capabilities tend to be informally specified (understanding, intentionality, reasoning, agency, creativity, selfhood, sentience, sapience, abstraction, common sense) but can also apply to specific tasks (categorizing an object as the Charles river, winning at Go).
MACHINE LEARNING
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Epistemological Challenges When Denying AI Capabilities
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When capability of a class of Turing complete systems is denied ("a computer/perceptron/transformer can never do X") it poses interesting epistemological and metaphysical challenges, which are unfortunately rarely discussed ("how can anything do X without breaking physics?").
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CellVerse: LLM Benchmark for Single-Cell Biology Tasks
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10. CellVerse Introduces a benchmark to evaluate LLMs on single-cell biology tasks by converting multi-omics data into natural language.
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RL Framework Teaches LLMs Efficient Search Tool Usage
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7. RL for Search-Efficient LLMs Proposes a new RL-based framework (SEM) that explicitly teaches LLMs when to invoke search and when to rely on internal knowledge, aiming to reduce redundant tool use while maintaining answer accuracy.
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Nemotron-Research-Tool-N1: LLM Tool-Using with Rule-Based RL
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6. Nemotron-Research-Tool-N1 Introduces Tool-N1, a family of tool-using LLMs trained using a rule-based reinforcement learning (R1-style RL) approach, without reliance on supervised reasoning trajectories.
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AM-Thinking-v1: 32B Open-Source Model Rivaling Larger MoE Systems
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4. AM-Thinking-v1 Introduces a dense, open-source 32B language model that achieves state-of-the-art performance in reasoning tasks, rivaling significantly larger Mixture-of-Experts (MoE) models.
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LLMs Performance Degradation in Multi-Turn Conversations
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2. LLMs Get Lost in Multi-Turn Conversation Investigates how top LLMs degrade in performance during underspecified, multi-turn interactions, common in real-world usage but rarely evaluated.
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RL Improves LLM Mathematical Reasoning with Single Example
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3. RL for Reasoning in LLMs with One Training Example This paper shows that Reinforcement Learning with Verifiable Rewards (RLVR) can significantly improve mathematical reasoning in LLMs even when trained with just a single example.
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Real Innovation Over Performative Tech Discourse
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All facts no fiction. Most of the noise is cosplay. We not here to audition we here to architect. Appreciate the real ones like you who get it. The rest? Let the curtain drop.
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Join VISION IA Community for AI Learning and More
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